Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal sampling of Gabor features for face recognition
Pattern Recognition Letters
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Boosting local binary pattern (LBP)-Based face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
IEEE Transactions on Image Processing
Local descriptors and similarity measures for frontal face recognition: A comparative analysis
Journal of Visual Communication and Image Representation
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Face verification technology is widely used in public safety, e-commerce, access control, and so on. We propose a novel face verification approach, which combines a relatively new object descriptor--Histogram of Gabor Phase Patterns (HGPP), AdaBoost Algorithm selecting HGPP features and learning binary classifier, and Quotient Image method synthesizing face images under new illumination conditions. Although Gabor wavelets have been widely used in face recognition, previous studies mainly focus on the magnitude information of Gabor feature, while neglect the phase information of it. We use HGPP as an attempt to utilize the neglected Gabor phase information in face verification. Then AdaBoost algorithm trains binary classifiers, meanwhile significantly reduce the dimension of HGPP. Further, the novel strategy that synthesizes and extends training samples with Quotient Image method enhances our algorithm's robustness for illumination variation. Experiments demonstrate our novel approach is able to achieve promising face verification results under different illumination conditions.